What is Atrial Fibrillation (AF)?
The most common abnormal heart rhythm is atrial fibrillation, an irregular heartbeat that affects over 46 million people worldwide and dramatically increases risks for stroke and heart failure. Common risk factors for atrial fibrillation (AF) include classical risks like obesity, aging, and hypertension.
In spite of progress in diagnosis and treatment of AF-related heart conditions and stroke, dramatic improvements in therapeutics require a deeper understanding of the genetic and cellular underpinnings of AF. Although AF is highly heritable, a high resolution genomics analysis of causative genes has yet to deliver actionable insights.
Genomics of Atrial Fibrillation
One factor plaguing genome wide association studies (GWASs) performed for AF (and for many GWASs across diseases) is a lack of population diversity. Existing AF GWASs have been predominantly performed in European populations. Thus, the resulting genetic insights, including downstream polygenic risk scores, are not robustly applicable to non-European populations.
In a recent paper in Nature Genetics, Miyazawa et. al. took a two-fold approach to improve the genetic understanding of AF. First, they performed a large-scale GWAS in a Japanese population to identify AF-related genetic variants in that population. Then, they performed a cross-ancestry meta-analysis to uncover a more robust, accurate picture of AF genetics.
A New Polygenic Risk Score for Heart Failure and Stroke
Based on their analysis of 150,272 Japanese individuals, the authors identified rare variants specific to AF in East Asian populations. However, when combined with existing GWASs, the authors’ cross-ancestry meta-analysis identified 35 new potential susceptibility loci for AF – specifically IL6R, an immune-related gene, when integrated with transcriptome-wide association data.
The authors further investigated functional and ChIP-seq data to identify ERRg as a key transcriptional regulator of genes associated with AF. Finally, they developed a robust PRS for cardiovascular and stroke mortality based on the data from their cross-ancestry analysis which was able to stratify individuals who suffered from cardioembolic stroke but were undiagnosed for AF at that point. This study highlights the importance of diversity and variety in samples for GWAS and other genetic studies to differentiate between common causative variants and variants specific to a certain population, and opens new research targets for atrial fibrillation diagnosis and treatment.
Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help
Our clients are using cutting-edge bioinformatics tools including GWAS, ChIP-seq and more to answer pressing biological research questions. From pipeline development and software engineering to deploying existing bioinformatics tools, Bridge Informatics can help you on every step of your research journey.
As experts across data types from cutting-edge sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. Click here to schedule a free introductory call with a member of our team.
Jane Cook, Biochemist & Content Writer, Bridge Informatics
Jane Cook, leading Content Writer for Bridge Informatics, has written over 100 articles on the latest topics and trends for the bioinformatics community. Jane’s broad and deep interdisciplinary molecular biology experience spans developing biochemistry assays to genomics. Prior to joining Bridge, Jane held research assistant roles in biochemistry research labs across a variety of therapeutic areas. While obtaining her B.A. in Biochemistry from Trinity College in Dublin, Ireland, Jane also studied journalism at New York University’s Arthur L. Carter Journalism Institute. As a native Texan, she embraces any challenge that comes her way. Jane hails from Dallas but returns to Ireland any and every chance she gets. If you’re interested in reaching out, please email jennifer.martinez@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.
